


import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    TextToVideoSDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

@skip_mps
class     A__   (       snake_case__			,   unittest.TestCase      ):







    """simple docstring"""

    __magic_name__         =						TextToVideoSDPipeline
    __magic_name__         =						TEXT_TO_IMAGE_PARAMS
    __magic_name__         =						TEXT_TO_IMAGE_BATCH_PARAMS
    # No `output_type`.
    __magic_name__         =						frozenset(
        [
            'num_inference_steps',
            'generator',
            'latents',
            'return_dict',
            'callback',
            'callback_steps',
        ]      )
    def 						a_     (			self       ):
         torch.manual_seed(0       )
         snake_case											=							UNetaDConditionModel(
             block_out_channels=(3_2, 6_4, 6_4, 6_4)     ,   layers_per_block=2     ,   sample_size=3_2     ,   in_channels=4     ,   out_channels=4     ,   down_block_types=('''CrossAttnDownBlock3D''', '''CrossAttnDownBlock3D''', '''CrossAttnDownBlock3D''', '''DownBlock3D''')     ,   up_block_types=('''UpBlock3D''', '''CrossAttnUpBlock3D''', '''CrossAttnUpBlock3D''', '''CrossAttnUpBlock3D''')     ,   cross_attention_dim=3_2     ,   attention_head_dim=4     ,   )
         snake_case											=							DDIMScheduler(
             beta_start=0.0_0085     ,   beta_end=0.012     ,   beta_schedule='''scaled_linear'''     ,   clip_sample=__snake_case     ,   set_alpha_to_one=__snake_case     ,   )
         torch.manual_seed(0       )
         snake_case											=							AutoencoderKL(
             block_out_channels=[3_2, 6_4]     ,   in_channels=3     ,   out_channels=3     ,   down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']     ,   up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']     ,   latent_channels=4     ,   sample_size=1_2_8     ,   )
         torch.manual_seed(0       )
         snake_case											=							CLIPTextConfig(
             bos_token_id=0     ,   eos_token_id=2     ,   hidden_size=3_2     ,   intermediate_size=3_7     ,   layer_norm_eps=1E-05     ,   num_attention_heads=4     ,   num_hidden_layers=5     ,   pad_token_id=1     ,   vocab_size=1_0_0_0     ,   hidden_act='''gelu'''     ,   projection_dim=5_1_2     ,   )
         snake_case											=							CLIPTextModel(__snake_case       )
         snake_case											=							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )

         snake_case											=							{
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''vae''': vae,
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
         }
         return components
    def 						a_     (			self     ,   __snake_case     ,   __snake_case=0       ):
         if str(__snake_case       ).startswith('''mps'''       ):
              snake_case											=							torch.manual_seed(__snake_case       )
         else:
              snake_case											=							torch.Generator(device=__snake_case       ).manual_seed(__snake_case       )
         snake_case											=							{
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''guidance_scale''': 6.0,
             '''output_type''': '''pt''',
         }
         return inputs
    def 						a_     (			self       ):
         snake_case											=							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
         snake_case											=							self.get_dummy_components()
         snake_case											=							TextToVideoSDPipeline(**__snake_case       )
         snake_case											=							sd_pipe.to(__snake_case       )
         sd_pipe.set_progress_bar_config(disable=__snake_case       )

         snake_case											=							self.get_dummy_inputs(__snake_case       )
         snake_case											=							'''np'''
         snake_case											=							sd_pipe(**__snake_case       ).frames
         snake_case											=							frames[0][-3:, -3:, -1]

         assert frames[0].shape == (6_4, 6_4, 3)
         snake_case											=							np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
    def 						a_     (			self       ):
         self._test_attention_slicing_forward_pass(test_mean_pixel_difference=__snake_case     ,   expected_max_diff=3E-3       )
    @unittest.skipIf(
        torch_device != '''cuda''' or not is_xformers_available()     ,   reason='''XFormers attention is only available with CUDA and `xformers` installed'''     ,   )
    def 						a_     (			self       ):
         self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__snake_case     ,   expected_max_diff=1E-2       )
    @unittest.skip(reason='''Batching needs to be properly figured out first for this pipeline.'''       )
    def 						a_     (			self       ):
         pass
    @unittest.skip(reason='''Batching needs to be properly figured out first for this pipeline.'''       )
    def 						a_     (			self       ):
         pass
    @unittest.skip(reason='''`num_images_per_prompt` argument is not supported for this pipeline.'''       )
    def 						a_     (			self       ):
         pass




    def 						a_     (			self       ):
         return super().test_progress_bar()






@slow
@skip_mps
class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    def 						a_     (			self       ):
         snake_case											=							load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy'''       )

         snake_case											=							TextToVideoSDPipeline.from_pretrained('''damo-vilab/text-to-video-ms-1.7b'''       )
         snake_case											=							DPMSolverMultistepScheduler.from_config(pipe.scheduler.config       )
         snake_case											=							pipe.to('''cuda'''       )

         snake_case											=							'''Spiderman is surfing'''
         snake_case											=							torch.Generator(device='''cpu'''       ).manual_seed(0       )

         snake_case											=							pipe(__snake_case     ,   generator=__snake_case     ,   num_inference_steps=2_5     ,   output_type='''pt'''       ).frames
         snake_case											=							video_frames.cpu().numpy()

         assert np.abs(expected_video - video       ).mean() < 5E-2




    def 						a_     (			self       ):
         snake_case											=							load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy'''       )

         snake_case											=							TextToVideoSDPipeline.from_pretrained('''damo-vilab/text-to-video-ms-1.7b'''       )
         snake_case											=							pipe.to('''cuda'''       )

         snake_case											=							'''Spiderman is surfing'''
         snake_case											=							torch.Generator(device='''cpu'''       ).manual_seed(0       )

         snake_case											=							pipe(__snake_case     ,   generator=__snake_case     ,   num_inference_steps=2     ,   output_type='''pt'''       ).frames
         snake_case											=							video_frames.cpu().numpy()

         assert np.abs(expected_video - video       ).mean() < 5E-2








from math import factorial

class     A__   :







    """simple docstring"""

    def __init__(			self     ,   __snake_case     ,   __snake_case       ):
         snake_case											=							real
         if isinstance(__snake_case     ,   __snake_case       ):
              snake_case											=							[1] * rank
         else:
              snake_case											=							rank
    def __repr__(			self       ):
         return (
             F'''{self.real}+'''
             F'''{"+".join(str(__snake_case       )+"E"+str(n+1       )for n,dual in enumerate(self.duals       )       )}'''
         )
    def 						a_     (			self       ):
         snake_case											=							self.duals.copy()
         while cur[-1] == 0:
              cur.pop(-1       )
         return Dual(self.real     ,   __snake_case       )
    def __add__(			self     ,   __snake_case       ):
         if not isinstance(__snake_case     ,   __snake_case       ):
              return Dual(self.real + other     ,   self.duals       )
         snake_case											=							self.duals.copy()
         snake_case											=							other.duals.copy()
         if len(__snake_case       ) > len(__snake_case       ):
              o_dual.extend([1] * (len(__snake_case       ) - len(__snake_case       ))       )
         elif len(__snake_case       ) < len(__snake_case       ):
              s_dual.extend([1] * (len(__snake_case       ) - len(__snake_case       ))       )
         snake_case											=							[]
         for i in range(len(__snake_case       )       ):
              new_duals.append(s_dual[i] + o_dual[i]       )
         return Dual(self.real + other.real     ,   __snake_case       )

    __magic_name__         =						__add__
    def __sub__(			self     ,   __snake_case       ):
         return self + other * -1
    def __mul__(			self     ,   __snake_case       ):
         if not isinstance(__snake_case     ,   __snake_case       ):
              snake_case											=							[]
              for i in self.duals:
                   new_duals.append(i * other       )
              return Dual(self.real * other     ,   __snake_case       )
         snake_case											=							[0] * (len(self.duals       ) + len(other.duals       ) + 1)
         for i, item in enumerate(self.duals       ):
              for j, jtem in enumerate(other.duals       ):
                   new_duals[i + j + 1] += item * jtem
         for k in range(len(self.duals       )       ):
              new_duals[k] += self.duals[k] * other.real
         for index in range(len(other.duals       )       ):
              new_duals[index] += other.duals[index] * self.real
         return Dual(self.real * other.real     ,   __snake_case       )

    __magic_name__         =						__mul__
    def __truediv__(			self     ,   __snake_case       ):
         if not isinstance(__snake_case     ,   __snake_case       ):
              snake_case											=							[]
              for i in self.duals:
                   new_duals.append(i / other       )
              return Dual(self.real / other     ,   __snake_case       )
         raise ValueError
    def __floordiv__(			self     ,   __snake_case       ):
         if not isinstance(__snake_case     ,   __snake_case       ):
              snake_case											=							[]
              for i in self.duals:
                   new_duals.append(i // other       )
              return Dual(self.real // other     ,   __snake_case       )
         raise ValueError




    def __pow__(			self     ,   __snake_case       ):
         if n < 0 or isinstance(__snake_case     ,   __snake_case       ):
              raise ValueError('''power must be a positive integer'''       )
         if n == 0:
              return 1
         if n == 1:
              return self
         snake_case											=							self
         for _ in range(n - 1       ):
              x *= self
         return x






def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     if not callable(UpperCamelCase_							):
          raise ValueError('''differentiate() requires a function as input for func'''							)
     if not isinstance(UpperCamelCase_				,(float, int)							):
          raise ValueError('''differentiate() requires a float as input for position'''							)
     if not isinstance(UpperCamelCase_				,UpperCamelCase_							):
          raise ValueError('''differentiate() requires an int as input for order'''							)
     snake_case											=							Dual(UpperCamelCase_				,1							)
     snake_case											=							func(UpperCamelCase_							)
     if order == 0:
          return result.real
     return result.duals[order - 1] * factorial(UpperCamelCase_							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 def      UpperCAmelCase__		(UpperCamelCase_							):



      """simple docstring"""






      return y**2 * y**4

 print(differentiate(f, 9, 2))




